A Modern Data Platform combines Traditional Business Intelligence, Big Data and Machine Learning. It combines structured and unstructured data from internal and external data sources in a batch and neartime fashion. We will see how to blend these different facets together, creating actionable insights, for you to provide to your users and to make downstream systems smarter.
Data are treated and stored in an uniform fashion, reducing friction to access and integrate it to ultimately extract its value. Data governance, retention and modelling is applied end-to-end at the level appropriate to the data's purpose.
Data from the data sources are captured raw, as-is, preserving all information, but provided to the consumers in the granularity and form as-needed. We will see the application of machine learning to source data that was inaccessible before.
Data and derived insights and their implementation are differentiated by how they are used. Distributing data, providing reports or insights through APIs pose significantly different requirements to a data platform. Using fit-for-purpose solutions make it easier to implement them.
We will provide a conceptual understanding how data flows through the platform and how to produce actionable insights to be used at the point of impact. Then we will peel back the covers and have a peek at exemplary implementations using services on AWS. We will also cover what principles are at play and why this is easier than expected. To make it your own we will provide a bottom up approach and top down approach to get started.